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The Frontiers of Society, Science and Technology ISSN 2616-7433
Vol. 2, Issue 11: 62-77, DOI: 10.25236/FSST.2020.021111
Published by Francis Academic Press, UK
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Customer Concentration, Level of Research and Development, and
Supplier’s Profitability Analysis Based on High-Tech Manufactory
Industry
Guo Jingwei1, Huang Siyao2, Yin Hang3
1 Shanghai Starriver Bilingual School, Shanghai, China 2
Shanghai University of International Business and Economics,
Shanghai, China 3 University of California Irvine, Irvine,
U.S.A.
ABSTRACT. We use multiple regression model to empirically
examine how high-tech manufactory supplier’s customer concentration
and research and development affect the supplier’s profitability,
and we also address the underlying mechanisms. Along with
hypothesis, we justify that higher high-tech manufactory supplier’s
customer concentration might lead to lower the supplier’s
profitability. Higher high-tech manufactory supplier’s customer
concentration might lead to higher customer’s relative bargaining
power. High-tech manufactory supplier’s higher level of R&D
might lead to higher the supplier’s profitability. Not along with
hypothesis, higher high-tech manufactory supplier’s customer
concentration is likely leading to lower the supplier’s risk and
lower supplier’s operating efficiency. High-tech manufactory
supplier’s higher research and development might lead to higher the
supplier’s risk. High-tech manufactory suppliers’ higher research
and development might lead to lower the supplier’s operating
efficiency. There might be an inverted-shape association between
high-tech manufactory supplier’s customer concentration and the
supplier’s level of research and development.
KEYWORDS: Customer concentration, Suppliers’ profitability,
Research and development, High-tech manufactory
1. Introduction
1.1 Previous Studies
Regarding customer concentration affecting firms’ overall
profitability, many literatures have disputed over whether customer
has a negative or positive effect on firm’s profitability overall,
under firms’ different life cycle, or under supplier’s different
market environment. (Patatoukas 2012) introduced that customer
concentration has a positive impact overall on firms’ profitability
through enhancement of asset utilization and reduction of SG&A
expenses.[1] (Irvine P J 2016) broadened the discussion of customer
concentration impacting firm’s financial performances in the firm’s
life cycle. [2] (Kelly &Gosman2000) drew a result that customer
concentration reduces profitability in perfectly competitive market
rather than in oligopoly. [3](Chang H, Hall C M, Paz M.
2017)discovered that the effect of competition on the relationship
between customer concentration and cost structure is isolated to
the COGS and COGM. “ [4]
Regarding research and development, and customer concentration
affecting supplier’s financial performance, (Louis Raymond and
Fosee 2004) introduced R&D intensity as a mediator that
connects the effect of customer concentration to supplier’s gross
margin. It’s proposed that commercially dependent SMEs allocate
their finance for higher R&D, and R&D intensity can allow
small firms to mediate the effect of high customer-based
concentration by reversing the direction of dependency. [5]
1.2 Results and Implications
We analyze whether customer concentration has a positive or
negative impact on the high-tech manufactory suppliers’ overall
financial performance (profitability) specifically from three
aspects including customer bargaining power, supplier’s operating
efficiencies, and supplier’s risk of bankruptcy. Also, we analyze
whether suppliers’ level of R&D leads to higher level of
suppliers’ profitability from three aspects including customer
bargaining power, supplier’s operating efficiencies, and supplier’s
risk of bankruptcy. Along with hypothesis, we justify that higher
high-tech manufactory supplier’s customer concentration might lead
to lower the supplier’s
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The Frontiers of Society, Science and Technology ISSN 2616-7433
Vol. 2, Issue 11: 62-77, DOI: 10.25236/FSST.2020.021111
Published by Francis Academic Press, UK
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profitability. Higher high-tech manufactory supplier’s customer
concentration might lead to higher customer’s relative bargaining
power. High-tech manufactory supplier’s higher level of R&D
might lead to higher the supplier’s profitability. Not along with
hypothesis, higher high-tech manufactory supplier’s customer
concentration is likely leading to lower the supplier’s risk and
lower supplier’s operating efficiency. High-tech manufactory
supplier’s higher research and development might lead to higher the
supplier’s risk. High-tech manufactory suppliers’ higher research
and development might lead to lower the supplier’s operating
efficiency. Also, we analyze the possible relationship between
customer concentration and supplier’s level of research and
development based on the two- way scatterplot. There might be an
inverted-U shape association between customer concentration and
supplier’s level of research and development.
Our results give insight for high-tech manufactory suppliers to
control customer-based concentration and level of research and
development to achieve the higher profitability.
2. Literature Review and Hypothesis Development
2.1 Customer Concentration and Suppliers’ Profitability
2.1.1 Customer Concentration and Customers’ Bargaining Power
Customer concentration has a positive relationship with
bargaining power. In high-tech industry, when the customer
concentration of a supplier is high, the bargaining power of their
major customer would be strong. To begin with, the bargaining power
of major customer may come from their ability to let the supplier
company to be more organized. Customer concentration makes
production and investment of suppliers’ become organized. A shift
in this imposes prompt and heavy losses and thus the threat or even
the fear of sanctions is enough to provide customers with
considerable bargaining power over transaction prices and trade
credit terms. The bargaining power of a major customer influences a
firm’s financial performance in many aspects by letting the
supplier to offer them lower prices, extend trade credits and carry
extra inventories. (Scherer 1970) [6]
Ha1: Customer Concentration is positively associated with
customer’s bargaining power
2.1.2 Customer Concentration and Suppliers’ Operating
Efficiency
Higher customer concentration predicts higher operational
efficiencies through reduced SG&A expenses, enhanced asset
utilization, which is reflected by enhanced turnover ratios and
shorted cash conversion cycle. Also, customer concentration is
associated with higher turnover rates in higher competitive market
structure. “Higher customer concentration is associated with higher
efficiency gains in the form of reduced SG&A expenses and
enhanced asset utilization. Suppliers with higher customer
concentration spend less on SG&A per dollar of sales, hold less
of their assets in inventory, experience higher asset turnover
rates and shorter cash conversion cycle.” [1]
Ha2: Customer Concentration is positively associated with
Suppliers’ Operating Efficiency
2.1.3 Customer Concentration and Suppliers’ Risks
The difference in bargaining power may lead to the risk of
financial performance reduction due to the high customer
concentration. Major customers with stronger bargaining power are
more likely to shift the risk of relationship-specific investments
to suppliers (Kang, Mahoney, and Tang 2009).[7] For example, large
customers may use the strong bargaining power they gain from their
position as a leading retailer to extend payment terms and transfer
some inventory risk to suppliers. (Gosman and Kelly 2003).[8] With
the intensification of competition in the market of suppliers'
products, the relative bargaining power between suppliers and
powerful customers decreases, because customers will have more
alternative suppliers to choose from, and the risk of customer loss
will also increase. Prior research highlights that suppliers
operating under higher competition face greater operating risks
when they have higher customer concentration because major
customers have more substitute suppliers to buy from (Dhaliwal et
al. 2016). [9] There is a different condition in specific
relationship, (Banejree et al 2008) thought that major customer
relationships are inherently risky and can break down in a few
years. [10] Such failures are expensive, especially for suppliers
of unique products, which are made to customer specifications
rather than to the market. Customer-specific SG&A expenditures
are less transferable than general SG&A investments, thus
increasing the firm's fixed SG&A costs, resulting in higher
operating leverage and greater negative earnings potential.
Therefore, Companies with high customer concentration are more
likely to suffer initial losses than those with low customer
concentration. Because of establishing and maintaining
relationships with major customers requires large, fixed
investments early in the relationship, firms in the relationship
life cycle will face significant operating risks. (Irvine et al
2016) [2]
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On the other hand, if major customers get into financial
trouble, suppliers will face risk of losing a lot of substantial
future sales. Hertzel, Li, Officer, and Rodgers (2008) also found
that if a major customer declares bankruptcy, the other remaining
major customers will question its viability because of the impact
of one of their major customer’s bankruptcy declaration, which may
make the firm get into a more terrible financial position. [11] At
the same time, (Jorion and Zhang 2009) found that suppliers that
provide more trade credit to their customers have seen large
negative abnormal returns on their shares around the time of the
announcement of bankruptcy.[12] So, If the customer goes bankrupt,
the supplier will suffer the risk of losing the expected cash flow
due to the failure to collect the receivables.
Finally, the supplier/customer investment loses value when the
firms go bankruptcy. In addition to the risk of liquidation, there
is a high degree of uncertainty about the terms of future
transactions, because the results of a particular-relationship
investment cannot be fully predicted in advance (Tirole
1999).[13]
Ha3: Customer Concentration is positively associated with
Suppliers’ Risks
There is a negative relationship between customer’s bargaining
power and the supplier’s financial performance. Major customers
would demand lower prices, purchase irregularly and may delay
payments. All those behaviors clearly led to a lower gross margin
of the suppliers, which later caused lower revenue as well as lower
profit. Industry-level measures of downstream bargaining power are
associated with lower upstream gross margin. (Lustgarten.1975,
LaFrance .1979, Ravenscraft. 1983). [14] Also, as we take the
relatively-week supplier into consideration, relatively more
powerful customers might use their power within the supply chain to
enhance their own performance by extracting the gains to trade from
interactions with their relatively weaker suppliers. (
Balakrishnan, Linsmeier, and Venkatachalam 1996; Cooper and
Slagmulder 2004; Gosman, Kelly, Olsson, and Warfield 2004; Gosman
and Kohlbeck 2009).[15] Additionally, bargaining power has caused
risk problems. Firms with greater uncertainties are less likely to
make payout.
There is positive relationship between operating efficiency and
suppliers’ profitability. Operating in a efficient way means
operating with less costs and gain more.
We do not know whether the customer bargaining power’s effect
will dominate the supplier operating efficiency effect. We assume
that customer bargaining power is dominating.
Ha: Customer Concentration is negatively associated with
suppliers’ profitability.
2.2 R&D and Supplier’s Profitability
In technology-intensive enterprises, there is a two-way
correlation between R&D and profitability. The purpose of
enterprises' investment in R&D is to obtain higher income,
which is consistent with expanding production capacity and reducing
production cost. The effect of R&D intensity is not to directly
affect the profitability of enterprises, but to affect the
profitability of enterprises by affecting productivity. For a
company with high productivity, there will be a significant
increase in profit margin in the future as the R&D intensity
increases. (Irvine et al 2016) says that the long-term nature of
the relationship facilitates the information sharing that benefits
the firm’s R&D productivity.[2]
In the information technology industry which most regard as a
technology intensive industry, R&D is the core competitiveness
of enterprises. On the one hand, if an enterprise has a high profit
level, it will reduce its survival pressure and invest more money
in research and development projects. On the other hand, if the
profitability of enterprises is low for a long time, the intensity
of R&D intensity will be weakened. Once this happens,
enterprises in this industry may be replaced, absorbed, or
eliminated. (Linghu2020).[16]
The relationship between the R&D and profitability is
complex because they affect each other and are reflected in
different financial indicators. In some cases, R&D and
enterprise profitability are positively correlated, but there is
also a negative correlation. (Raymond & St-Pierre 2004) said
that customer concentration seems to negatively affect
profitability, SMEs whose products R&D activity is more intense
reports significantly higher gross margin. R&D intensity is
found to counter the negative effect highly-based customers.[5]
(Shi 2019) But in technology-intensive enterprises, there is a
law generally, in the short term, the higher R&D intensity of
enterprises may negatively impact the profitability of the
enterprises, which is mainly due to the time cost of transformation
from R&D investment to the actual productivity, so the
investment of R&D on profitability has lag, in other words, it
cannot be shown on financial statement immediately. But after a
certain amount of time, R&D will show a positive effect.
[17]
Hb: R&D intensity is positively associated with suppliers’
profitability.
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2.3 Customer Concentration and Suppliers’ R&D
The relationship between customer concentration and R&D
intensity is always a debatable question. Marcin Krolikowski and
XiaoJing Yuan discuss this uncertain relationship from both sides
by considering customer bargaining power and supplier risk.
[18]
When they take customer bargaining power as the mediator, they
find out that the relationship between customer concentration and
R&D intensity is negative. It is clear that customer
concertation has a positive relationship with customer bargaining
power, and as customer bargaining power has a negative relationship
with R&D intensity of the supplier, and customer concentration
is positively associated with customer bargaining power, they get
to their first conclusion that customer concentration has a
negative relationship with R&D intensity of the supplier.
However, after that they then consider the risk arises from high
level of customer concentration. There is a positive relationship
between risk and level of R&D. As we know that high customer
concentration may cause a risk to the supplier, bringing
uncertainty to the future management of the supplier company, so
when the supplier aware of the high switching costs they need to
bear, they would invest more in R&D and innovation to make sure
their major customer do not go to somewhere else. Because customer
concentration is positively associated with risk, in this way, the
relationship between customer concentration and level of R&D
becomes positive.
There is no final conclusion in their paper, but Krokowski and
Yuan do give us a thinking that the relationship between customer
concentration and R&D intensity is variable.
A more specific idea from Xu Hong, Linzhong Gao and Rui Chen is
that the relationship between customer concentration and R&D
intensity is in an inverted-U shape when companies are non-group-
affiliated firms and the relationship becomes negative when
companies are group-affiliated firms. [19] Similarly, this
inverted-U shape still exists in regions with a higher level of
financial development, but the relationship takes on negative
correlation in regions with a lower level of financial development.
Their study also shows that as a kind of specific asset investment,
enterprise's R&D investment not only demonstrates its value of
signal transfer in terms of customer concentration, but also
reflects different intensity in different financing constraints
scenarios, highlighting its significant moderating effect.
Therefore, considering both the ideas from two papers above, we
suggested that the relationship between customer concentration and
R&D intensity is in U shape.
We admit that the relationship is variable according to
different factors, but we do not think it is in an inverted-U
shape. And when customer concentration was high enough, it would be
more sensible for the supplier to do more specific investment and
increase the R&D intensity for their major customer.
Hc: There is a U shape relationship between customer
concentration and suppliers’ level of R&D.
Fig.1 Hypothesized Relationships between Variables
3. Empirical Analysis
3.1 Summary Statistics
In the study, we adopted data of high-tech manufactory firms of
different years. The Standard Industrial Classification (SIC) are
four-digit codes that categorize the industries that companies
belong to while organizing the industries by their business
activities. SIC codes of the chosen high-tech manufactory firms
range from 3570 to
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3579.
The following are explanations/calculations of all
variables.
Explained Variables:
(1) PM: Profit margin. Profit margin is one of the commonly used
profitability ratios.
It represents what percentage of sales has turned into profits.
Higher supplier’s profit margin represents higher supplier’s
profitability.
PM=gross profit/gross sales
GM: Gross margin. Gross margin is the sales revenue a company
retains after incurring the direct costs associated with producing
the goods it sells, and the services it provides. Higher supplier’s
gross margin represents higher supplier’s profitability.
GM=operating income after considering depreciation/sales
(2) ROA: Return on asset. Return on Assets (ROA) is an indicator
of how well a company utilizes its assets, by determining how
profitable a company is relative to its total assets. Higher
supplier’s ROA indicates more asset efficiency and higher
supplier’s profitability.
ROA=net income/total assets
(3) ROE: Return on equity. ROE is considered a measure of how
effectively management is using a company’s assets to create
profits. Return on equity (ROE) is a measure of financial
performance calculated by dividing net income by shareholders'
equity. Higher supplier’s ROE indicates more net asset efficiency
and higher supplier’s profitability.
ROE=(total assets/shareholders’ equity)*ROA
=(total assets/shareholders’ equity)*( net income/total
assets)
=net income/shareholders’ equity
(4) %rtopc: percent total revenue from the top customer. Higher
supplier’s %rtopc indicates higher top customer’s relative
bargaining power since the supplier has it total revenue highly
relied on the top customer.
(5) %rmainc: percent total revenue from major customers. Higher
supplier’s %rmainc indicates higher major customers’ relative
bargaining power since the supplier has it total revenue highly
relied on the major customers.
(6) Altman Z: The Altman Z-score is the output of a
credit-strength test that gauges a publicly-traded manufacturing
company's likelihood of bankruptcy.
Z-Score = 1.2A + 1.4B + 3.3C + 0.6D + 1.0E
A = working capital / total assets
B = retained earnings / total assets
C = earnings before interest and tax / total assets
D = market value of equity / total liabilities
E = sales / total assets
(7) FAT: Fix asset turnover. The fixed asset turnover ratio
(FAT) is used to measure operating performance. This efficiency
ratio compares net sales (income statement) to fixed assets
(balance sheet) and measures a company's ability to generate net
sales from its fixed- asset investments, namely property, plant,
and equipment (PP&E). A higher supplier’s FAT indicates that a
company has effectively used investments in fixed assets to
generate sales, so as supplier’s higher operating efficiency.
FAT= annual total sales/ (total Net Property and equipment at
the start of a year+ total Net Property and equipment at the end of
a year)/2
(8) TAT: Total asset turnover. The asset turnover ratio measures
the value of a company's sales or revenues relative to the value of
its assets. The asset turnover ratio can be used as an indicator of
the efficiency with which a company is using its assets to generate
revenue. The higher the asset turnover ratio, the more efficient a
company is at generating revenue from its assets, so as supplier’s
higher operating efficiency.
TAT=Annual Total sales/ (Assets at the start of a year+ Assets
at the end of a year)/2 Explanatory Variables:
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(1) R&D Intensity: Research and development intensity. It is
one of the indicators for science and technology devotion.
R&D intensity= R&D Spending/Revenue
(2) R&D Spend: Research and development spending. It is one
of the indicators for science and technology devotion.
(3) CC: Customer concentration. customer concentration is a
measure of how total revenue is distributed among your customer
base. A company serving a large number of small- volume customers
has a lower customer concentration than a firm where a handful of
large customers account for the majority of its business.
(4) Rank (CC): Rank customer concentration. I use decile rank
transformations. Firms are ranked annually and assigned to deciles
based on CC. The raw values of DCC are replaced by the
corresponding annual decile ranks, scaled to lie between 0 (lowest
rank) and 1 (highest rank). Controlled Variables:
(5) Age: Firm’s age.
(6) IHI: Industry Herfindahl Index.
(7) SG: Sales growth.
(8) FL: Financial leverage.
(9) CONGLO: Whether the supplier is a conglomerate. If the firm
is a conglomerate, its’ CONGLO is “1”. If the firm is not a
conglomerate, its’ CONGLO is “0”.
Table 1 Summary Statistics of Key Variables for Modeling
Customer Concentration, Level of R&D and Suppliers’ Financial
Performances
Variable Observations Mean Standard Deviation Minimum Maximum PM
182 .4747384 .1865316 -.0995946 .9134635 GM 182 -.87391 .2675241
-1.475582 .2376818 ROA 182 -.1280759 .3120161 -2.548673 .3630815
ROE 182 .5628993 11.6929 -10.47619 156.5043 R&D Spend 182
258.2428 954.414 0 5942 R&D Intensity 182 .1795633 .2081931 0
1.574089 CC 182 .1739339 .1774318 .0098526 .9375398 Rank(CC) 182
.507326 .2811288 0 1 AGE 182 17.60989 9.829289 1 40 IHI 182
.0471246 .0035637 .0388658 .051162 SG 182 .1201613 .4221662
-.4724318 2.842147 FL 182 -.4478263 30.6232 -409.1478 16.62603
CONGLO 182 .4065934 .4925528 0 1
Table 2 Summary Statistics of Key Variables for Modeling
Customer Concentration, Level of R&D and Customer Bargaining
Power
Variable Observations Mean Standard Deviation Minimum Maximum CC
152 .1608585 .166322 .0098526 .9025028 Rank(CC) 152 .4912281
.274119 0 1 R&D Intensity 152 .1551419 .1545196 0 .9992547
R&D Spend 152 256.1768 950.5023 0 5942 TAT 152 1.048963
.6099982 .0201611 3.863379 AGE 152 18.67763 9.495626 2 40 SG 152
.0894265 .3189258 -.4724318 1.335308 FL 152 -.9179222 33.47542
-409.1478 16.62603 CONGLO 152 .4276316 .4963706 0 1 IHI 152
.0468914 .0035479 .0388658 .051162
Table 3 Summary Statistics of Key Variables for Modeling
Customer Concentration, Level of R&D and Suppliers’ Risk of
Bankruptcy
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Variable Observations Mean Standard Deviation Minimum Maximum CC
162 .1760372 .182677 .0098526 .9375398 Rank(CC) 162 .5068587
.2829244 0 1 R&D Intensity 162 .1406544 .1264314 0 .9992547
R&D Spend 162 288.5435 1007.786 0 5942 Altman Z 162 -1.828678
20.86901 -227.6751 14.3272 AGE 162 18.72222 9.396874 1 40 SG 162
.0551476 .2683929 -.4724318 1.147195 FL 162 -.5949228 32.44638
-409.1478 16.62603 CONGLO 162 .4567901 .499674 0 1 IHI 162 .0471454
.0035677 .0388658 .051162
Table 4 Summary Statistics of Key Variables for Modeling
Customer Concentration, Level of R&D and Suppliers’ Operating
Efficiency (Fix Asset Turnover)
Variable Observations Mean Standard Deviation Minimum Maximum CC
148 .1580172 .182677 .0098526 .9025028 Rank(CC) 148 .484985
.2829244 0 1 R&D Spend 148 262.5218 .1264314 0 5942 R&D
Intensity 148 .1524702 1007.786 0 .9992547 FAT 148 26.94352
20.86901 .1924444 178.5669 AGE 148 9.411357 9.396874 2 40 SG 148
.3196075 .2683929 -.4724318 1.335308 FL 148 33.92425 32.44638
-409.1478 16.62603 CONGLO 148 .496124 .499674 0 1 IHI 148 .0035255
.0035677 .0388658 .051162
Table 5 Summary Statistics of Key Variables for Modeling
Customer Concentration, Level of r&d and Suppliers’ Operating
Efficiency (Total Asset Turnover)
Variable Observations Mean Standard Deviation Minimum Maximum CC
152 .1608585 .166322 .0098526 .9025028 Rank(CC) 152 .4912281
.274119 0 1 R&D Intensity 152 .1551419 .1545196 0 .9992547
R&D Spend 152 256.1768 950.5023 0 5942 TAT 152 1.048963
.6099982 .0201611 3.863379 AGE 152 18.67763 9.495626 2 40 SG 152
.0894265 .3189258 -.4724318 1.335308 FL 152 -.9179222 33.47542
-409.1478 16.62603 CONGLO 152 .4276316 .4963706 0 1 IHI 152
.0468914 .0035479 .0388658 .051162
3.2 Empirical Modeling and Robustness Check
We use multiple regression models to examine the relationship
between Customer Concentration, Level of R&D and Suppliers’
financial performances, the relationship between Customer
Concentration, Level of R&D and Customer Bargaining Power, the
relationship between Customer Concentration, Level of R&D and
Suppliers’ Risks, and the relationship between Customer
Concentration, Level of R&D and Suppliers’ Operating
Efficiency.
3.2.1 Customer Concentration, Research and Development, and
Suppliers’ Profitability
Regarding the relationship between Customer Concentration, Level
of R&D and Suppliers’ financial performances, we include gross
margin(GM), profit margin(PM), return on assets (ROA), return on
equity (ROE) as explained variables, customer concentration(CC)and
R&D Intensity (R&D Intensity) each as explanatory variable,
and age of the firm(AGE),Industry Herfindahl Index(IHI), sales
growth(SG), financial leverage(FL), whether or not
conglomerate(CONGLO) as controlled variables. To check for the
robustness of the relationship between Customer Concentration,
Level of R&D and Suppliers’ financial performances, we
introduce Rank
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Customer Concentration (Rank (CC)) and R&D Spending (R&D
Spend) each as a new response variable. (ε) is the error term
accounting for random disturbances.
Table 6 Customer Concentration, Research and Development, and
Suppliers’ Profitability
(1) (2) (3) (4) VARIABLES GM PM ROA ROE CC 0.0164 -0.146**
-0.134 0.439 (0.0830) (0.0672) (0.119) (0.405) R&D Intensity
-0.984*** 0.498*** -0.675*** -1.836*** (0.0904) (0.0732) (0.129)
(0.440) AGE 0.00245 -0.000338 8.53e-05 -0.00508 (0.00170) (0.00137)
(0.00243) (0.00827) IHI 4.810 3.062 3.135 -5.340 (4.233) (3.424)
(6.047) (20.62) SG 0.266*** -0.0743** 0.263*** 0.593*** (0.0426)
(0.0345) (0.0609) (0.208) FL 0.000104 0.000395 0.000436 -0.381***
(0.000475) (0.000384) (0.000679) (0.00231) CONGLO 0.0514 -0.0524*
0.121** 0.655*** (0.0330) (0.0267) (0.0471) (0.161) Constant -0.236
0.303* -0.213 0.649 (0.207) (0.167) (0.295) (1.007) Observations
182 182 182 182 R-squared 0.495 0.320 0.242 0.994
Standard errors in parentheses *** p
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(4.204) (3.446) (6.021) (20.54) SG 0.268*** -0.0705** 0.263***
0.587*** (0.0427) (0.0350) (0.0612) (0.209) FL 0.000105 0.000363
0.000411 -0.381*** (0.000474) (0.000389) (0.000679) (0.00232)
Conglo 0.0527 -0.0473* 0.123*** 0.645*** (0.0329) (0.0270) (0.0472)
(0.161) Constant -0.243 0.328* -0.184 0.561 (0.206) (0.169) (0.296)
(1.009) Observations 182 182 182 182 R-squared 0.496 0.303 0.239
0.994
Standard errors in parentheses *** p
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The following are the descriptions of the regressions results
displayed in table 9.
When we use R&D Spend as the explanatory variable, we
discover negative association between customer concentration and
suppliers’ profit margin at the significance level of 99%. We find
positive associations between suppliers’ R&D Spend and all the
listed financial performance indicators at 99% confidence
level.
We have enough evidence to conclude that customer concentration
is negatively associated with suppliers’ profitability, and
suppliers’ level of R&D is positively associated with
suppliers’ financial performances.
3.2.2 Customer Concentration, Research and Development and
Customers’ Bargaining Power
Regarding the relationship between Customer Concentration, Level
of R&D and Customer Bargaining Power, we include percent total
revenue from the top customer (%rtopc) as explained variables,
customer concentration(CC)and R&D Intensity (R&D Intensity)
each as explanatory variable, and age of the firm(AGE),Industry
Herfindahl Index(IHI), sales growth(SG), financial leverage(FL),
whether or not conglomerate(CONGLO) as controlled variables. To
check for the robustness of the relationship between Customer
Concentration, Level of R&D and Customer Bargaining Power, we
introduces Rank Customer Concentration (Rank(CC)) ,R&D Spending
(R&D Spend) each as a new explanatory variable, and percent
total revenue from major customers (%rmainc) as a new response
variable. (ε) is the error term accounting for random
disturbances.
Table 9 Customer Concentration, Research and Development and Top
Customer’s Bargaining Power
VARIABLES (1)%rtopc (2)%rtopc (3)%rtopc (4)%rtopc CC R&D
Intensity
0.899*** (0.0493) -0.156***
-0.228*** 0.894*** (0.0490)
(0.0537) (0.0611) AGE 0.00127 -0.000324 0.00140 0.000315
(0.00101) (0.00116) (0.000992) (0.00118) SG 0.0550** 0.0618**
0.0161 0.00429 (0.0253) (0.0288) (0.0213) (0.0250) FL -0.000237
-8.41e-05 -0.000288 -0.000141 (0.000282) (0.000320) (0.000280)
(0.000329) CONGLO 0.0159 0.0121 0.0341* 0.0334 (0.0196) (0.0222)
(0.0190) (0.0223) HHI 0.936 4.093 1.333 4.655 RANK(CC) R&D
Spend
(2.515) (2.832) 0.528*** (0.0356)
(2.495) 3.00e-05***
(2.915) 0.511*** (0.0368) 1.85e-05*
Constant
0 .0704
-0.148
(9.01e-06) 0.0115
(1.07e-05) -0.225
(0.123) (0.139) (0.121) (0.142) Observations 182 182 182 182
R-squared 0.677 0.585 0.681 0.560
Standard errors in parentheses *** p
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When we use R&D spend as the explanatory variable, we
discover a positive relationship between R&D spend and percent
total revenue from top customer at 99% confidence level, and there
is a positive relationship between customer concentration and
percent total revenue from top customer at 99% confidence
level.
When we use Rank(CC) and R&D spend as the explanatory
variable, we discover that there is a positive relationship between
Rank(CC) and percent total revenue from top customer at 99%
confidence level, and there is a positive relationship between
R&D spend and percent total revenue from top customer at 90%
confidence level.
We have enough evidence to conclude that customer concentration
is positively associated with the top customers’ bargaining power.
However, it seems that the relationship between suppliers’ R&D
level and the top customer’s bargaining power is not robust
enough.
Table 10 Customer Concentration, Research and Development and
Major Customers’ Bargaining Power
VARIABLES (1)%rmainc (2)%rmainc (3)%rmainc (4)%rmainc CC R&D
Intensity
1.148*** (0.0662) 0.152**
0.0453
1.149*** (0.0669)
(0.0721) (0.0615) AGE 0.000943 -0.00181 0.000557 -0.00163
(0.00135) (0.00117) (0.00135) (0.00113) SG -0.0458 -0.0281 -0.00790
-0.0166 (0.0340) (0.0290) (0.0291) (0.0239) FL -0.000165 1.76e-05
-0.000127 4.35e-05 (0.000379) (0.000322) (0.000382) (0.000315)
CONGLO 0.0257 0.0290 0.0109 0.0212 (0.0263) (0.0224) (0.0259)
(0.0213) HHI -5.083 -1.622 -5.408 -1.839 RANK(CC) R&D Spend
(3.374) (2.853) 0.787*** (0.0359)
(3.407) -1.05e-05
(2.787) 0.800*** (0.0352) -2.98e-05***
Constant
0.477***
0.178
(1.23e-05) 0.530***
(1.02e-05) 0.196
(0.165) (0.140) (0.165) (0.136) Observations 182 182 182 182
R-squared 0.645 0.743 0.638 0.754
Standard errors in parentheses *** p
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customers’ bargaining power is not robust enough.
In conclusion, customer concentration is positively associated
with customers’ bargaining power. While, the relationship between
R&D level and customers’ bargaining power is undetermined.
3.2.3 Customer Concentration, Research and Development and
Suppliers’ Operating Efficiency
Regarding the relationship between Customer Concentration, Level
of R&D and Suppliers’ Operating Efficiency, we include total
assets turnover(TAT) and fix assets turnover (FAT) each as the
explained variable, customer concentration(CC)and R&D Intensity
(R&D Intensity) each as explanatory variable, and age of the
firm(AGE),Industry Herfindahl Index(IHI), sales growth(SG),
financial leverage(FL), whether or not conglomerate(CONGLO) as
controlled variables. To check for the robustness of the
relationship between Customer Concentration, Level of R&D and
Suppliers’ Operating Efficiency, we introduces Rank Customer
Concentration(Rank(CC)) , R&D Spending (R&D Spend) each as
a new explanatory variable.(ε) is the error term accounting for
random disturbances.
Table 11 Customer Concentration, Research and Development and
Suppliers’ Operating Efficiency
(1) (2) (3) (4) (5) (6) (7) (8) VARIABLES
TAT TAT TAT TAT FAT FAT FAT FAT
CC
0.00969
0.117
-19.52
-16.87
(0.284) (0.284) (13.80) (13.59) R&D Intensity
-1.074***
-1.071***
-18.52 -15.75
AGE
(0.333) 0.0198***
(0.331) 0.0212***
0.0246***
0.0257***
(16.72) 0.453*
(16.44) 0.551**
0.580**
0.654**
(0.00537)
(0.00544)
(0.00532) (0.00539) (0.262) (0.263) (0.254) (0.254)
SG 0.140 0.118 0.0135 -0.00826 2.383 1.096 0.823 -0.134 (0.154)
(0.154) (0.148) (0.148) (7.395) (7.301) (7.071) (6.983) FL -0.00193
-0.00190 -0.00212 -0.00208 -0.140*
* -0.142**
-0.143** -0.144**
(0.00137)
(0.00137)
(0.00137) (0.00137) (0.0658) (0.0647) (0.0647) (0.0638)
CONGLO -0.349***
-0.366***
-0.283*** -0.299*** -20.38***
-21.17***
-19.75***
-20.59***
(0.103) (0.103) (0.100) (0.0999) (4.970) (4.882) (4.766) (4.707)
HHI -6.746 -6.585 -6.067 -5.323 -368.7 -458.5 -447.4 -519.1 (13.35)
(13.23) (13.36) (13.26) (646.2) (633.4) (637.6) (626.2)
RANK(CC)
-0.183 -0.120 -21.24**
-19.09**
R&D Spend
(0.173) -0.000153***
(0.176) -0.000147***
(8.354) -0.00538**
(8.324) -0.00482**
(4.84e-05) (4.87e-05) (0.00229) (0.00227) Constant 1.295**
1.363** 1.013 1.042 50.14 59.79* 49.53 58.41* (0.655) (0.655)
(0.644) (0.646) (31.56) (31.26) (30.73) (30.50) Observations
152 152 152 152 148 148 148 148
R-squared 0.202 0.208 0.200 0.201 0.155 0.181 0.180 0.201
Standard errors in parentheses *** p
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𝛼𝛼7CONGLO+ε FAT= 𝛼𝛼0 + 𝛼𝛼1Rank(CC)+ 𝛼𝛼2R&D Intensity +
𝛼𝛼3AGE+ 𝛼𝛼4IHI+ 𝛼𝛼5SG+ 𝛼𝛼6FL+ 𝛼𝛼7CONGLO+ε FAT= 𝛼𝛼0 + 𝛼𝛼1CC+
𝛼𝛼2R&D Spend+ 𝛼𝛼3AGE+ 𝛼𝛼4IHI+ 𝛼𝛼5SG+ 𝛼𝛼6FL+ 𝛼𝛼7CONGLO+ε FAT=
𝛼𝛼0 + 𝛼𝛼1Rank(CC)+ 𝛼𝛼2R&D Spend+ 𝛼𝛼3AGE+ 𝛼𝛼4IHI+ 𝛼𝛼5SG+ 𝛼𝛼6FL+
𝛼𝛼7CONGLO+ε The following are the descriptions of the regressions
results displayed in table 12
There is no significant relationship between customer
concentration and total asset turnover. R&D intensity is
negatively associated with TAT at a confidence level of 99%. There
is no significant relationship between CC and TAT.
When we use Rank (CC) as the explanatory variable, we discover
no significant relationship between Rank (CC) and TAT, and R&D
intensity is negatively associated with TAT at a confidence level
of 99%. When we use R&D Spend as the explanatory variable, we
discover no significant relationship between CC and TAT, and
R&D spend is negatively associated with TAT at a confidence
level of 99%.
When we use Rank (CC) and R&D spend as explanatory
variables, we discover no significant relationship between Rank(CC)
and TAT, and R&D spend is negatively associated with TAT at a
confidence level of 99%.
We have enough evidence to conclude that there is a negative
association between suppliers’ R&D intensity and suppliers’
total assets turnover. While, the relationship between customer
concentration and total assets turnover is not significant.
There is no significant relationship between customer
concentration and fix asset turnover. There is no significant
relationship between R&D intensity and FAT.
When we use Rank (CC) as the explanatory variable, we discover
negative relationship between Rank (CC) and total asset turnover at
a confidence level of 95%, and there is no significant relationship
between R&D intensity and FAT.
When we use R&D Spend as the explanatory variable, we
discover negative relationship between R&D spend and FAT at a
confidence level of 95%, and there is no significant relationship
between CC and FAT.
When we use Rank (CC) and R&D spend as explanatory
variables, we discover a negative relationship between Rank (CC)
and FAT at a confidence level of 95%, and R&D spend is
negatively associated with FAT at a confidence level of 95%.
We have enough evidence to conclude that there is a negative
association between suppliers’ level of R&D and suppliers’ fix
asset turnover. While, there is a negative association between
customer concentration and suppliers’ fix asset turnover.
In conclusion, there is a negative association between
suppliers’ R&D level and suppliers’ operating efficiencies, and
there is a negative association between customer concentration and
suppliers’ operating efficiencies.
3.2.4 Customer Concentration, Research and Development and
Suppliers’ Risks
Regarding the relationship between Customer Concentration, Level
of R&D and Suppliers’ Risks, we include Altman Z score (Altman
Z) as the explained variable, customer concentration(CC)and R&D
Intensity (R&D Intensity) each as explanatory variable, and age
of the firm(AGE),Industry Herfindahl Index(IHI), sales growth(SG),
financial leverage(FL), whether or not conglomerate(CONGLO) as
controlled variables. To check for the robustness of the
relationship between Customer Concentration, Level of R&D and
Suppliers’ Risks, we introduce Rank Customer Concentration (Rank
(CC)), R&D Spending (R&D Spend) each as a new explanatory
variable. (ε) is the error term accounting for random
disturbances.
Table 12 Customer Concentration, Research and Development and
Suppliers’ Risks
VARIABLES
(1) Altman Z
(2) Altman Z
(3) Altman Z
(4) Altman Z
CC
-28.15*** (9.136)
-28.25*** (8.890)
R&D Intensity -2.577 -1.862 (13.33) (13.39) AGE 0.0291
0.0286 0.0909 0.0920 (0.195) 13.14** (0.186) 11.64* (0.200) 12.82**
(0.191) 11.24*
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SG (6.296) (6.150) (6.332) (6.178) FL -0.00163 0.00228 -0.00605
-0.00196 (0.0505) (0.0492) (0.0506) (0.0492) CONGLO 0.178 1.421
0.203 1.420 (3.602) (3.501) (3.617) (3.511) HHI 212.4 347.1 112.0
250.9 R&D Spend Rank(CC) (484.4) (473.2)
-0.00459*** (0.00159)
(482.9) -17.48***
(471.3) -0.00471*** (0.00160) -17.96***
(6.046) (5.874) Constant -8.072 -13.86 -0.684 -6.315 (23.55)
(22.70) (23.57) (22.70) Observations 157 157 157 157 R-squared
0.098 0.145 0.091 0.141
Standard errors in parentheses *** p
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Fig.2 Customer Concentration and Research and Development
There seems an inverted U shape relationship between Rank (CC)
and R&D Intensity.
4. Conclusion
4.1 Results and Implications
Based on empirical analysis, we find that higher customer
concentration might lead to lower overall high-tech manufactory
suppliers’ profitability. Higher customer concentration might lead
to higher customer bargaining power. Higher level of R&D might
lead to higher high-tech manufactory suppliers’ profitability. What
is interesting is that higher customer concentration is likely
leading to lower high-tech manufactory suppliers’ risk and
operating efficiencies. High-tech manufactory suppliers’ higher
research and development might lead to higher risk. Higher research
and development level might lead to lower suppliers’ operating
efficiency.
There might be an inverted-shape association between customer
concentration and suppliers’ level of research and development.
However, we do not know whether higher customer concentration cause
suppliers’ level of research and development to change because we
cannot conclude that it’s higher suppliers’ risk causing suppliers’
lower level of R&D, that it’s higher operating efficiency
causing suppliers’ lower level of R&D.
Fig.3 Relationships between Variables Based on Empirical
Analysis
4.2 Limitations and Further Studies
From the sampling standpoint, we chose high-tech manufactory
firms which SIC codes range from 3570 to
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3579. Further studies might enlarge the sample.
For the indicator of suppliers’ risk, we only use Altman Z score
which measures the suppliers’ like-hood of bankruptcy. There might
be other indicators to measure suppliers’ risks. Further studies
might include more variables indicating for suppliers’ risks. For
the indicator of suppliers’ operating efficiencies, we only include
total asset turnover and fix asset turnover. There are other
indicators to measure suppliers’ operating efficiency. Further
studies might include more variables indicating for suppliers’
operating efficiency.
We only study the potential association between customer
concentration and level of research and development, association
doesn’t imply causation. Further studies might identify possible
controlled variables influencing suppliers’ decision to change
level of R&D, so that causation relationship might be
discovered. Also, regarding the customer concentration and level of
research and development, further studies can include moderating
variables.
Further studies might explain why higher level of high-tech
manufactory suppliers’ research and development might lead to lower
like-hood of the suppliers’ bankruptcy and why higher high-tech
manufactory suppliers’ level of research and development might lead
to lower suppliers’ operating efficiency.
Regarding the suppliers’ level of R&D and suppliers’
profitability, further studies might consider time lag.
Acknowledgement
Thanks for Mr. Michael Paz’s provision of data.
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1. Introduction1.1 Previous Studies1.2 Results and
Implications
2. Literature Review and Hypothesis Development2.1 Customer
Concentration and Suppliers’ Profitability2.1.1 Customer
Concentration and Customers’ Bargaining Power2.1.2 Customer
Concentration and Suppliers’ Operating Efficiency2.1.3 Customer
Concentration and Suppliers’ Risks
2.2 R&D and Supplier’s Profitability2.3 Customer
Concentration and Suppliers’ R&D
3. Empirical Analysis3.1 Summary Statistics3.2 Empirical
Modeling and Robustness Check3.2.1 Customer Concentration, Research
and Development, and Suppliers’ Profitability
Table 6 Customer Concentration, Research and Development, and
Suppliers’ ProfitabilityTable 7 Customer Concentration, Research
and Development Intensity, and Suppliers’ Profitability Robustness
Check 1Table 8 Customer Concentration, Research and Development
Intensity, and Suppliers’ Profitability Robustness Check 23.2.2
Customer Concentration, Research and Development and Customers’
Bargaining PowerTable 9 Customer Concentration, Research and
Development and Top Customer’s Bargaining PowerTable 10 Customer
Concentration, Research and Development and Major Customers’
Bargaining Power3.2.3 Customer Concentration, Research and
Development and Suppliers’ Operating EfficiencyTable 11 Customer
Concentration, Research and Development and Suppliers’ Operating
Efficiency3.2.4 Customer Concentration, Research and Development
and Suppliers’ RisksTable 12 Customer Concentration, Research and
Development and Suppliers’ Risks3.2.5 Customer Concentration and
Research and DevelopmentFig.2 Customer Concentration and Research
and Development4. Conclusion4.1 Results and Implications4.2
Limitations and Further Studies